Detection and mitigation of temporary impairments in a communications channel

ABSTRACT

Systems and methods are disclosed for detecting temporary high level impairments, such as noise or interference, for example, in a communications channel, and subsequently, mitigating the deleterious effects of the dynamic impairments. In one embodiment, the method not only performs dynamic characterization of channel fidelity against impairments, but also uses this dynamic characterization of the channel fidelity to adapt the receiver processing and to affect an improvement in the performance of the receiver. For example, in this embodiment, the method increases the accuracy of the estimation of the transmitted information, or similarly, increases the probability of making the correct estimates of the transmitted information, even in the presence of temporary severe levels of impairment. The channel fidelity history may also be stored and catalogued for use in, for example, future optimization of the transmit waveform.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application makes reference to, and claims priority to and thebenefit of, U.S. provisional application Ser. No. 60/296,884 filed Jun.8, 2001, which application is hereby incorporated herein by reference inits entirety.

FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[Not Applicable]

MICROFICHE/COPYRIGHT REFERENCE

[Not Applicable]

BACKGROUND OF THE INVENTION

The present invention applies to communications systems, all of whichare inherently limited in their capacity (or rate) of informationtransfer by channel impairments. One example of an impairment is oftenreferred to by the generic term “noise.” Noise sometimes emanates, forexample, from within electrical components themselves, such asamplifiers and even passive resistors. Another example of an impairmentis referred to as “interference,” which is usually taken to be someunwanted manmade emission perhaps for another communications system suchas radio, or perhaps from switching circuits in a home or automobile.“Distortion” is a further example of an impairment, and includes lineardistortion in the channel, such as pass-band ripple or non-flat groupdelay, and nonlinear distortion, such as compression in an overdrivenamplifier. Of course, there are many other types of impairments that mayadversely affect communications in a channel.

Often in communications channels, the impairments may by dynamic innature. In many cases, the impairment level may be at one level ofseverity most of the time, and the communications system may be designedor optimized (in some fashion) to operate at that level of impairment.Occasionally, however, one or more of the impairments may rise to sosevere an amount as to preclude the operation of the communicationssystem optimized for the more ordinary level of impairments.

In prior art, in some applications where a large interferer or burst ofnoise occasionally impinges upon the receiver, the received signal issimply blanked during increased power to mitigate large out-of-theordinary bursts of received power. Often, analog processing means areused, almost at, if not right at, the receiver input. Sometimes this isdone especially to protect sensitive receiver front-ends from damage.While this technique may provide some benefit in circumstances where thenoise or interference power dwarfs the signal-of-interest power, it doesnot protect against the many other impairments which have power more onthe order of the signal-of-interest power (or even much less). Also, byitself, this blanking does not provide the receiver with a means toimprove its overall performance in the presence of the lost information,i.e., the information content concurrent with the large noise burst.

Other prior art that may have been applied to this problem, evenunknowingly, is the use of forward error correction (FEC) techniquesthat incorporate soft-decision decoding, such as is common withconvolutional error correction codes and the Viterbi decoding algorithm.In this prior art, as the error power in the received signal isincreased, this increase is passed directly into the decision process.Such encoding and decoding techniques have been in common practice foryears, and are widely applied without thought to temporary fidelitychanges in the channel. Fortunately, in the event of a change in thechannel fidelity, the soft-decision decoding will automatically takeinto consideration the larger error power in making signal decisions.However, unfortunately, often with a change in channel conditions, thereis a duration of multiple symbol intervals (in a digital communicationssystem example) where the degradation persists, and during this timesome symbols may be erred so severely that they actually appear close toanother possible (but wrong) symbol. In this event, which becomes muchmore likely as the constellation density (of a QAM constellation, forexample) is increased for high rate communications, the soft-decisiondecoder actually “thinks” it has received a low error power, and mayrate the wrong signal with a high confidence.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of such systems with the present invention as set forth inthe remainder of the present application with reference to the drawings.

BRIEF SUMMARY OF THE INVENTION

Features of the present invention may be found in a method of impairmentmitigation for use in a communications system. In one embodiment, themethod comprises generating one or more error power estimates of asignal. The error power estimate(s) are then used to determine a channelfidelity metric, which in turn is used to decode the signal. The signalmay comprise, for example, one or more digital samples in an analogcommunications example, or one or more symbols in a digitalcommunications example.

In one embodiment, the channel fidelity metric is stored for use infuture communication. For example, the stored fidelity metric may beused to determine a transmit waveform and/or to select a receiveralgorithm.

In one embodiment involving digital communications, the error powerestimate(s) is/are generated by determining a constellation point(s)closest to the symbol(s), and squaring the distance between thesymbol(s) and the constellation point(s).

In one embodiment, determining a channel fidelity metric comprisescomparing the error power estimate(s) to a predetermined threshold(s),and generating a first indication (e.g., indicating a channel degradedcondition) if the error power estimate(s) is/are above the predeterminedthreshold(s), and a second indication (e.g., indicating a channel OKcondition) if the error power estimate(s) is/are not above thepredetermined threshold(s). A select symbol, i.e., one underconsideration, is kept if the error power estimate(s) is/are below thepredetermined threshold(s), and is erased if the error power estimate(s)is/are above the threshold(s). The signal is then decoded with theerasures.

In another embodiment, a branch metric is generated, and then modifiedbased on the channel fidelity metric. For example, the branch metric isset to a low probability if the fidelity metric indicates a degradedchannel. The signal is then decoded using the modified branch metric ina Viterbi decoder, for example.

These and other advantages and novel features of the present invention,as well as details of an illustrated embodiment thereof, will be morefully understood from the following description and drawings.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 illustrates a block diagram of a generic communication systemthat may be employed in connection with one embodiment of the presentinvention.

FIG. 2 is a block diagram of an impairment mitigation system inaccordance with one embodiment of the present invention.

FIG. 3 is a flow diagram illustrating one embodiment of a method thatmay be performed using the system of FIG. 2, in accordance with thepresent invention.

FIG. 4 is a flow diagram of another embodiment of a method that may beperformed using the system of FIG. 2, in accordance with the presentinvention, where channel impairments are learned and catalogued in anefficient and pertinent manner, for future study and improvement ofcommunications waveforms and processing using that channel.

FIG. 5 is a flow diagram illustrating a method of impairment mitigationin accordance with one specific embodiment of the present invention.

FIG. 6 is a block diagram of an impairment mitigation system inaccordance with another embodiment of the present invention.

FIGS. 7A-7B are a flow diagram illustrating a method of impairmentmitigation in accordance with one specific embodiment of the presentinvention, for use in connection with digital communications.

FIGS. 8A-8B are a flow diagram illustrating a method of impairmentmitigation in accordance with another specific embodiment of the presentinvention, for use in connection with digital communications.

FIG. 9 is a flow diagram illustrating a method that uses a fidelitymetric to modify branch metrics in the decoding process, in accordancewith one embodiment of the present invention.

FIG. 10 is a block diagram of an impairment mitigation system that usespreliminary decoding in generating error power estimates, in accordancewith one embodiment of the present invention.

FIG. 11 is a flow diagram illustrating one embodiment a method ofimpairment mitigation that may be employed using the system of FIG. 10.

DETAILED DESCRIPTION OF THE INVENTION

The following description is made with reference to the appendedfigures.

FIG. 1 illustrates a block diagram of a generic communication systemthat may be employed in connection with one embodiment of the presentinvention. The system comprises a first communication node 101, a secondcommunication node 111, and at least one channel 109 thatcommunicatively couples the nodes 101 and 111. The communication nodesmay be, for example, cable modems, DSL modems or any other type oftransceiver device that transmits or receives data over one or morechannels.

The first communication node 101 comprises a transmitter 105, a receiver103 and a processor 106. The processor 106 may comprise, for example, amicroprocessor. The first communication node 101 is communicativelycoupled to a user 100 (e.g., a computer) via communication link 110, andto the channel 109 via communication links 107 and 108. Of course,communication links 107 and 108 may be combined into a singlecommunication link.

Similarly, the second communication node 111 comprises a transmitter115, a receiver 114 and a processor 118. The processor 118, likeprocessor 106, may comprise, for example, a microprocessor. The secondcommunication node 111 likewise is communicatively coupled to the atleast one channel 109 via communication links 112 and 113. Again, likecommunication links 107 and 108, the communication links 112 and 113 mayalso be combined into a single communication link. The communicationnode 111 may also be communicatively coupled to a user 120 (again acomputer, for example) via communication link 121. In the case whencommunication node 111 is a headend, for example, user 120 may not bepresent.

During operation of one embodiment of FIG. 1, the user 100 cancommunicate information to the user 120 using the first communicationnode 101, the at least one channel 109 and the second communication node111. Specifically, the user 100 communicates the information to thefirst communication node 101 via communication link 110. The informationis transformed in the transmitter 105 to match the restrictions imposedby the at least one channel 109. The transmitter 105 then communicatesthe information to the at least one channel 109 via communication link107.

The receiver 114 of the second communication node 111 next receives, viacommunication link 113, the information from the at least one channel109 and transforms it into a form usable by the user 120. Finally, theinformation is communicated from the second communication node 111 tothe user 120 via the communication link 121.

Communication of information from user 120 to user 100 may also beachieved in a similar manner. In either case, the informationtransmitted/received may also be processed using the processors 106/118.

FIG. 2 is a block diagram of an impairment mitigation system 200 inaccordance with one embodiment of the present invention. The system 200may be contained, for example, in one or both of the communication nodesof FIG. 1. Error power estimates on a sample by sample basis may begenerated for analog modulations. A receiver 201 receives at input 203an input of either noise (when no signal is present) or a signal withtime varying distortion and/or noise, for example. The receiver 201 usesthe input to generate error power estimates, and may do so either on abit by bit basis or using a sequence of bits (or on a symbol by symbolbasis or using a sequence of symbols, in a digital communicationsexample). A sliding window 205 receives the error power estimates. Theerror power estimates are processed in a fidelity processor 207 and ametric for channel fidelity is continuously generated as the window(i.e., time) progresses. The behavior of the metric versus time may becatalogued (see catalogue 209) and/or analyzed and used to optimize thetransmission waveform, and it may be used to enhance receiverperformance in real-time, or near-real time, or even in apost-reception, post-processing mode.

A delay 208 between the input error power estimates of the window 205and the corresponding channel fidelity metric is known for a givenfidelity processor, and is provided back (made known) to a remainder ofthe system. The system uses the evolving fidelity metric in itsprocessing, which may be aided by soft decisions (reference numeral211). Soft decisions comprise, for example, erasure decoding or standardsoft decision decoding, such as Viterbi decoding. In any case, thereceiver outputs an estimate of the transmitted signal (referencenumeral 213).

While FIG. 2 illustrates a system having some components andfunctionality located outside of the receiver, it should be understoodthat such system may have additional components or functionality locatedwithin the receiver, or may in fact be entirely contained within thereceiver. In addition, it should also be understood that the estimationof the error power and the processing shown as being performed withinthe receiver of FIG. 2, may instead be performed outside of thereceiver.

FIG. 3 is a flow diagram illustrating one embodiment of a method thatmay be performed using the system of FIG. 2, in accordance with thepresent invention. In one operation of the method, the error power of aninput to the system is estimated (block 301). A fidelity metric isdetermined, using the error power estimate (block 303). The fidelitymetric determined is then used to decode the input (block 305). Themethod of FIG. 3 may be employed on a limited basis, such as only duringthe presence of the signal of interest, for example, or may be employedcontinuously. In other words, the method specifically identified in FIG.3 may be employed in a continuous loop type fashion, for a limited orextended period of time. In either case, the error power of the input isestimated over time, and fidelity metrics are determined (each using oneor more error power estimates of the input) and used to decode the inputover time.

FIG. 4 is a flow diagram of another embodiment of a method that may beperformed using the system of FIG. 2, in accordance with the presentinvention. In one operation of the method, the error power of an inputto the system is estimated (block 401). A fidelity metric is determined,using the error power estimate (block 403). The fidelity metricdetermined is then saved for future use in communications (block 405).Like the method of FIG. 3, the method of FIG. 4 may be employed on alimited basis, such as only during time periods when no signal ofinterest is present, for example, or may be employed continuously. Inother words, the method specifically identified in FIG. 4 may, like thatof FIG. 3, be employed in a continuous loop type fashion, for a limitedor extended period of time. In either case, the error power of the inputis estimated over time, fidelity metrics are determined (each using oneor more error power estimates of the input) and information about thefidelity metrics is stored for future use in communications.

Specifically, for example, the stored information about the fidelitymetrics may be used in transmit waveform optimization. In other words,the information may be used to determine a waveform that best suits thechannel given what has been learned about the channel over time, asreflected in the stored fidelity metrics. The stored information aboutthe fidelity metrics may also (or alternatively) be used in selectingreceiver algorithms that are robust given the limitations of thechannel, again as reflected in the stored fidelity metrics. Additionaldetail regarding use of catalogued channel fidelity metric informationfor future communications is discussed below.

In one embodiment of the present invention, the methods discussed abovewith respect to FIGS. 3 and 4 may be used in conjunction. For example,the method of FIG. 3 may be employed when a signal of interest ispresent, while the method of FIG. 4 may be used when no signal ofinterest is present.

The error power estimates discussed above with respect to FIGS. 2-4 maybe generated in a multiplicity of ways, in the presence or absence of asignal of interest. In the absence of a signal of interest, the power ofthe receiver input may simply be the noise power. Filtering to thesignal of interest bandwidth may be used if desirable.

In an embodiment where the system of FIG. 2 is a digital communicationssystem, one particular approach for gathering the error power estimatesduring signaling is to calculate the distance (squared, for power)between the received signal and the nearest constellation point in thedigital system's signaling alphabet. This error vector is typicallyavailable or readily obtainable from a slicer in a digitalcommunications receiver.

The length of sliding window 205 of FIG. 2 is important in its selectionand application, but in general, may be any length. A shorter window isa subset of a longer window, so longer and longer windows cantheoretically provide better and better channel fidelity metrics.However, in practice, the window length should, for example, 1) be sizedto accommodate a given (tolerable) amount of delay (acceptable to therest of the receiver processing) in generating the channel fidelitymetric, 2) not unnecessarily increase the complexity of the overallreceiver, and 3) account for the durations or dynamics expected, orpreviously observed, in the dominating channel impairments. For example,if a transitory channel impairment has a duration of at most 10 symbolsin a given digital communications system, then it is hard to justify theuse of a window of 100 symbols. Similarly, a window of only 4 symbols,with the expectation of a persistence of 10 symbols of a givenimpairment condition, needlessly lessens the ability of the fidelityprocessor to make the best channel fidelity assessment, since it isdenied relevant (correlated) information regarding the channel fidelity.

The processing of the sequence of error power estimates in the fidelityprocessor 207 of FIG. 2 may also take on many forms, depending on thecomplexity allowed, the size of the sliding window or duration orpersistence of the impairment states, the delay allowed in generatingthe channel fidelity metrics, and on the use of the channel fidelitymetric (i.e., the accuracy of the metric in matching the impairmentlevel).

In its most simple form, the fidelity processor 207 may simply compareeach error power estimate against a threshold, and output a binarychannel fidelity estimate—i.e., “channel OK,” and “channel degraded.”While the window in this case consists of a single sample (or a singlesymbol in the digital communications example), the use of the catalogueof this information, and the beneficial use of this metric in subsequentreceiver processing, may be employed in one embodiment of the presentinvention (such as shown in FIG. 2 and discussed with respect to FIGS. 3and 4, for example).

FIG. 5 is a flow diagram illustrating a method of impairment mitigationin accordance with one specific embodiment of the present invention, foruse in connection with digital communications. First, a symbol isreceived (block 501), and the closest constellation point to the symbolis determined (block 503). As mentioned above, the closest constellationpoint may be determined from a slicer in the receiver. Next, the errorpower of the symbol is calculated using, for example, the square of thedistance between the received signal and the nearest constellation pointin the digital system's signaling alphabet, also as mentioned above(block 505). The error power of the symbol is then compared to athreshold of error power (block 507). This is performed, for example, inthe fidelity processor. If it is determined that the error power isgreater than the threshold, then it is assumed that the channel isdegraded, and the symbol is erased (block 509). If instead it isdetermined that the calculated error power of the symbol is not abovethe threshold, then it is assumed that the channel is OK, and the symbolis kept (block 511). In either case, the decision is communicated to thedecoder (block 513). In other words, if the symbol is kept (block 511),the symbol is simply communicated to the decoder (block 513), whereas ifthe symbol is erased (block 509), an indication that the symbol has beenerased is communicated to the decoder (block 513). This process isrepeated for each symbol received.

While the method of FIG. 5 is shown to be performed on a symbol bysymbol basis, it should be understood that multiple symbols may beconsidered. Moreover, the method may be employed using different meansfor calculating the error power (such as discussed herein), anddifferent processing may be used to determine whether or not the channelis OK or whether a particular symbol(s) should be erased or kept (alsosuch as discussed herein). In addition, the method may be employed inconnection with analog communications, using samples rather thansymbols.

As mentioned above with respect to FIG. 4, channel fidelity metricinformation obtained from the fidelity processor may be stored and usedfor future communications. In the particular example of FIG. 5, byanalyzing the duty factor of the “channel OK” versus the “channeldegraded” condition, and by analyzing the relative persistence of theseconditions, the transmitting waveform can be adapted to theseparameters. The appropriate amount of parity in FEC coding, and the bestchoice of interleaver parameters in FEC employing interleaving, arestrongly related to these parameters.

Similarly, as mentioned above with respect to FIG. 3, the receiver canmake use of this information directly. In the example of digitalcommunications, the receiver marks the bits corresponding to the“channel degraded” condition as having very low confidence in subsequentFEC decoding. Reed-Solomon codes are known for accommodating both errorcorrection and erasure marking in their decoding. By markingReed-Solomon symbols that contain bits transmitted during “channeldegraded” conditions as erasures, the decoder has a benefit of moreinformation than a typical Reed-Solomon decoder working only with harddecisions. In other words, using the side information about the channelfidelity, the decoder can produce better results (i.e., higher rate ofcorrect decoding). A Reed-Solomon decoder can accommodate twice as manyerased symbols as it can correct erred symbols, so finding instances ofdegraded channel fidelity which often lead to erred Reed-Solomon symbolsbenefits the decoder, and marking these as erasures, greatly benefitsthe decoder. If nearly all of the Reed-Solomon symbol errors areattributable to the degraded channel, and if the degraded channel isfairly accurately detected (in the fidelity processor), then almosttwice as many instances of the degraded condition can be tolerated.

Other fidelity processor examples include summing the error powerestimates in the sliding window, and providing these or a scaled version(such as an average) as the channel fidelity estimate. Alternately, thissum or average can itself be quantized into a binary decision, or afinite number of levels (such as “channel pristine,” “channel OK,” and“channel degraded” in one example), or even compressed, via a squareroot operation, for example. If a dominant channel impairment isexpected to persist for a duration of many symbols, then summing theerror power estimates for at least several symbols increases theaccuracy of the channel fidelity metric, especially during the “middle”of the impairment condition.

However, determining the precise moment when the degraded condition“turned on” and “turned off” may be difficult if a long window forsumming is used, without other modification. One approach for thissituation, where it is desired to increase the time-domain precision ofthe fidelity processor, is to compute the average error power during awindow, and apply two thresholds, one on the average and one onindividual samples of the error power estimates. The “channel degraded”assignment is only output at times corresponding to samples where theaverage error power in the window exceeded threshold #1, AND (a) thesample was between two samples which exceeded threshold #2, or (b) thesample was the only sample in the window which exceeded threshold #2.

Once again, a particular example would be to employ the summing of thenoise power estimates within the window, as just described, and comparethis result with a threshold. This binary channel fidelity metric isthen associated with the middle sample of the window (i.e., the delaycorresponds to half the window duration). With Reed-Solomon FEC, asbefore, the “channel degraded” association with any bits in aReed-Solomon symbol result in that symbol being marked for erasure inthe decoding process. The method described above can again be applied toenhance the time-domain precision of the channel fidelity metric.

FIG. 6 is a block diagram of an impairment mitigation system 600 inaccordance with this particular embodiment of the present invention. Thesystem 600 (like system 200 of FIG. 2) may be contained, for example, inone or both of the communication nodes of FIG. 1. Referring to FIG. 6, areceiver 601 receives at input 603 an input of signal and/or noise, plusan occasional high-level noise burst, for example. The receiver, usingslicer 605 and block 607, generates error power estimates, and may do soeither on a bit by bit basis or using a sequence of bits (or on a symbolby symbol basis or using a sequence of symbols, in a digitalcommunications example, or sample-by-sample in an analog waveform). Asliding window 609, shown as a 7 tap delay line, receives the errorpower estimates, which are then processed in a fidelity processor 611.The fidelity processor 611 continuously generates a metric for channelfidelity as the window (i.e., time) progresses.

Specifically, 7 tap delay line 609 captures 7 consecutive error powerestimates at a time, and computes an average error power using the 7captured estimates. In addition, the highest (maximum) error power ofthe first 4 captured estimates is determined (i.e., estimates 1 through4), and the highest (maximum) error power of the last 4 capturedestimates (i.e., estimates 4 through 7) is likewise determined. Next, adetermination is made whether the average error power calculated isgreater than a first threshold, and whether both maximum error powersare greater than a second threshold. If any one is not above itsrespective threshold, then a “channel OK” indication is sent to thereceiver 601. If all three are above their respective thresholds, then a“channel degraded” indication is sent to the receiver 601. Thisindication may be a simple 1 bit channel fidelity metric (e.g., a “1”for channel OK and a “0” for channel degraded). In a digitalcommunications example, the fidelity processor 611 generates a 1-bitchannel fidelity metric over time for QAM constellations, for example.

The receiver 601 receives the channel fidelity metric (reference numeral613) and is aware of the 4 sample or symbol delay (reference numeral615). Processing block 617, knowing the channel fidelity metric and theparticular sample or symbol being considered from the known delay,either erases the particular sample or symbol being considered(corresponding to a “channel degraded” fidelity metric), or keeps theparticular sample or symbol being considered (corresponding to a“channel OK” fidelity metric). This process is repeated so that theerror power estimate corresponding to each sample or symbol isconsidered by the fidelity processor 611. In the embodiment of FIG. 6,the particular sample or symbol being considered by the fidelityprocessor 611 is that corresponding to the error power estimate found atthe 4^(th) position in the 7 tap delay line 609 (and hence the 4 sampleor symbol delay).

A decoder 619, such as, for example, a Reed-Solomon Decoder, decodes thesamples or symbols with erasures, as determined by the fidelityprocessor 611. Many different types of algorithms may be used in thefidelity processor to generate fidelity metrics. Decoded data, that is,an estimate of the transmitted signal, is then output at output 621 ofthe receiver 610. It should be understood that the functionality ofprocessing block 617 may be part of the decoder 619. It should also beunderstood that means other than as shown in, or specifically discussedwith respect to, FIG. 6 may be used to calculate error power and togenerate the fidelity metric. Further, quantities other than 7 may beused for the tap delay line.

In addition, while FIG. 6 illustrates a system having some componentsand functionality located outside of the receiver, it should beunderstood that such system may have additional components orfunctionality located within the receiver, or may in fact be entirelycontained within the receiver. In addition, it should also be understoodthat the estimation of the error power and the processing shown as beingperformed within the receiver of FIG. 6, may instead be performedoutside of the receiver.

FIGS. 7A-7B are a flow diagram illustrating a method that may beemployed using the system of FIG. 6, in a digital communicationsembodiment of the present invention. A sequence of symbols is received(block 701), and the closest constellation point to each symbol isdetermined (block 703). The error power of each symbol is calculated,for example, using the square of the distance between the receivedsymbol and the nearest constellation point in the digital system'ssignaling alphabet, as mentioned above (block 705). Of course, othermethods of calculating or estimating the error power of each symbol maybe used.

Next, the error power of a sequence of symbols is captured (block 707),and an average power of the captured sequence is calculated (block 709).In addition, a maximum error power from a first portion of the capturedsequence is determined (block 711), and a maximum error power from asecond portion of the sequence is likewise determined (block 713). Thefirst and second portions of the sequence each include a common symbolthat is the “middle” symbol of the whole sequence (i.e., the last symbolof the first portion and the first symbol of the second portion). Inother words, for a sequence of length n, an odd number, the middlesymbol may be defined by 1+(n−1)/2. It is this number that defines thesymbol that is being considered as well as the symbol delay for decodingpurposes. Again, as mentioned above with respect to FIG. 6, the sequencelength may be 7, which makes symbol 4 of the sequence the symbol that isbeing considered, and defines the decoder delay to be 4 symbols. Ofcourse, even numbers may be used for window length, too, and the symbol(or sample) under consideration need not be the one in the center of thewindow. The use of an odd window length and center symbol (sample) forwhich the channel fidelity is being estimated is purely an example.

The average error power of the sequence is then compared to a firstthreshold (block 715). If the average is not above the first threshold,the common symbol is kept (block 717), otherwise, the maximum errorpower of the first portion of the sequence is compared to a secondthreshold (block 719). If that first maximum is not above the secondthreshold (block 719), the common symbol is kept (block 717), otherwise,the maximum error power of the second portion of the sequence iscompared to the second threshold (block 721). If that second maximum isnot above the first threshold, the common symbol is kept, otherwise, thecommon symbol is erased. In any case, the decision of whether to eraseor keep the common symbol is communicated to the decoder (block 725).The process is then repeated, so that each symbol received is at somepoint considered (i.e., each symbol received is the common symbol forone iteration of the process).

For a 16 QAM constellation example having a constellation RMS power of3.162 (i.e., the square root of 10) and, for example, a 7 symbolsequence, the first threshold may be 0, and the second threshold may be0.64, for example. Of course, the second threshold may be set to 0, suchthat just the average error power of the whole sequence is used.

While the decisions made by blocks 715, 719 and 721 of FIGS. 7A-7B areshown to be in a particular sequence, any order of those decisions maybe employed. In addition, those decisions may instead be performedsimultaneously, rather than sequentially, as shown in FIGS. 8A-8B.Specifically, decision block 801 of FIG. 8A replaces the decision blocks715, 719 and 721 of FIGS. 7A-7B. A single determination is made at block801 of FIG. 8A, based on the three comparisons, whether the commonsymbol should be erased or kept.

Another particular example for applying the channel fidelity metric toenhance the receiver processing follows with the summing of the errorpower over a sliding window. Especially with high densityconstellations, and with an impairment of low power or one such as gaincompression, where the impairment likely does not cause the received,distorted signal to fall outside the normal signaling constellation, thefidelity processor can determine the presence of the impairment, but asignificant fraction of the error power estimates may be rather small(since the received signal falls close to one of the many wrongsymbols). In these cases, even with convolutional coding FEC andtraditional Viterbi decoding, the branch metrics in the decoding processare not most accurately reflecting the state of the channel fidelitywhen they are simply the error power estimates or log of error powerestimates (for each symbol) from the slicer. Knowing that a degradedchannel condition existed even when a signal was received “close” to aconstellation point can be very beneficially used in the decoding,especially when “channel interleaving” is performed prior to thedecoding, thus dispersing the impacted symbols.

FIG. 9 is a flow diagram illustrating a method that uses a fidelitymetric to modify branch metrics in the decoding process, in accordancewith one embodiment of the present invention. First, a sequence ofsymbols is received (block 901), error power estimates are estimated ordetermined (block 903), and channel fidelity metrics are determinedusing the error power estimates (block 905). This may be achieved usingany means discussed herein, for example. In addition, branch metrics arecreated (block 907). For example, in a Viterbi decoder example, branchmetrics are created for the Viterbi decoder branches. (Scaled logarithmsof the error power are typically used). The Viterbi branches arenormally inversely related to the error power from various constellationsymbols, since the branch metrics represent the likelihood of the branchtransition.

Once branch metrics are created (block 907), the branch metrics aremodified based on the channel fidelity estimate (block 909). Forexample, the branch metric may be set to a low probability value if thechannel fidelity is determined to be poor. Finally, decoding (e.g.,Viterbi) is performed using the modified branch metric (block 911). Thisoverall process may then be repeated.

As mentioned above, various embodiments of the present invention providefor a fidelity processor that examines a sliding window of error powerestimates to yield a channel fidelity metric. While specific fidelityprocessing examples have been discussed above, still other types offidelity processing may be employed in connection with the variousembodiments of the present invention. For example, median filters orother ranking devices may be used. In a median filter, the middle rankedvalue within a window is output. Once again, as above, this value couldbe output “as is,” or quantized with various thresholds, perhaps into asingle binary output.

Other forms of nonlinear filtering may also serve as useful fidelityprocessors. For example, the error power estimates may be quantized to abinary level with a threshold, i.e., “1” for greater than threshold and“0” for less than threshold, and these quantized samples filtered oraveraged. This would simplify the “averaging” complexity, and a secondthreshold as described above could be applied to enhance the precisionof marking the “turn on” and “turn off” of the severe impairments.

Still other types of fidelity processing may include, for example, (1)summing, (2) ranking, (3) thresholding and summing, (4) summing andtwice thresholding (sum and individual points in the window), (5)quantizing the error power estimates or otherwise nonlinearly processingthem (e.g., square root or log), (6) averaging across the window andtaking the maximum of the average and (some factor multiplying) themiddle error power estimate in the window, (7) taking the maximum of themedian ranked value in the window and (some factor multiplying) themiddle error power estimate in the window, (8) nonlinearly processingthe error power estimates and averaging, and (9) quantizing the resultsfrom the aforementioned operations and/or nonlinearly processing them.

Further, the channel fidelity metric may be used to analyze channelbehavior, such as duration and fraction of time of impaired conditionscompared to unimpaired, especially for determining most suitable FEC andsymbol rates and constellation sizes, etc., for the dynamically varyingchannel. In addition, the channel fidelity metric may be applied to thereceiver for beneficial use of processing signals receivedcontemporaneous with the channel fidelity estimate. Some examples ofusing the channel fidelity metric to enhance receiver performanceinclude:

(1) marking Reed-Solomon symbols for erasure in a Reed-Solomon decodercapable of erasure and error correction decoding, and

(2) in convolutional coded FEC (or other soft-decision decoders, such asTurbo decoding), affecting the soft-decision metric for a symbol withthis additional channel fidelity metric.

This latter case especially benefits from this technique if channelinterleaving is performed on the symbol soft-decisions prior to thedecoding. Various embodiments of the present invention are especiallyeffective at enhancing receiver performance with severe impairmentduration of multiple symbols, and with high density signalingconstellations, as seen in these particular examples.

While the error power estimates discussed above have been generatedoutside of the decoding process, decoding may be used to generate apotentially improved error power estimate. In other words, anothermethod for generating the error power estimate is to actually perform apreliminary decoding (if FEC is employed), or partial decoding, andperform a better estimate of the transmitted waveform to more accuratelyestimate the error power. Such an approach means that there would bedelay in the generation of the error power estimates, but often this isnot a constraint. A second-pass at the decoding, now with the benefit ofthe channel fidelity metric (versus time) arising from the first-passerror power estimates, provides enhanced performance in the time-varyingimpairment scenario.

FIG. 10 is a block diagram of an impairment mitigation system 1000 thatuses preliminary decoding in generating error power estimates, inaccordance with one embodiment of the present invention. The system 1000may be contained, for example, in one or both of the communication nodesof FIG. 1. A receiver 1001 receives an input 1003, and performs normalhard and soft decisions. The information is then FEC decoded in FECdecoder 1005, and the information is then re-encoded by encoder 1007.The re-encoded information is then used along with the original input at1003, to generate an error estimate (reference numeral 1009), which inturn is used to calculate an error power estimate (reference numeral1011). A fidelity processor 1013 uses the error power estimate togenerate a channel fidelity metric, such as discussed above. FEC decoder1015 uses the channel fidelity metric, along with the original, delayedinput to decode the input, and output decoded data, i.e., an estimate ofthe transmitted signal, at output 1017. Of course, it should beunderstood that FEC decoder 1005 and FEC decoder 1015 may be combinedinto a single decoder.

FIG. 11 is a flow diagram illustrating one embodiment of a method ofimpairment mitigation that may be employed using the system of FIG. 10,for example. One or more symbols are received (block 1101), are decoded(block 1103) and then encoded (block 1105). The error power of thereceived signal(s) is then estimated using the encoded symbol(s) (block1107). A channel fidelity metric is then determined using the errorpower estimate(s) (block 1109), and the symbol(s) are decoded using thechannel fidelity metric determined (block 1111). If at block 1103 it isdetermined that particular received symbol(s) cannot be decoded and thusre-encoded, then those particular symbols are simply erased forestimation of error, for example.

As can be seen, the system of FIG. 10 and method of FIG. 11 determine afidelity metric after an initial decoding, and use it to perform asubsequent decoding. Multiple iterations of this process may bebeneficial in some cases.

Based on the above, various embodiments of the present invention providemeans to characterize the transitory nature of the impairments; i.e., todevelop knowledge, characterizing not just typical or even averagelevels of an impairment, but an understanding and characterization ofthe dynamic behavior of the impairment. With this knowledge, it ispossible to facilitate improved communications in the channel, either byadjusting the transmission signal design, or by altering or adjustingthe receiver processing, or both.

If the dynamic nature of the impairments is so rapid that it transitionsfrom benign to severe and back to benign again, faster than the receivercan determine and communicate back to the original transmitter thisdegradation in the channel, then any adjustments in the transmissionwaveform are “permanent,” in the sense that adaptation to thetemporarily degraded channel is precluded by the dynamics. Still, theoptimal transmission waveform may be different if and when it is learnedthat the channel contains some severe but transitory impairment(s).Thus, it benefits the communications system to learn and characterizethe transitory nature of the impairments, by leading to a superiortransmit waveform with this new knowledge.

While some situations may preclude the feedback and adjustment of thetransmit waveform for adapting to a temporary increase of an impairment,in such situations, the receiver may still benefit from this knowledge.

Many modifications and variations of the present invention are possiblein light of the above teachings. Thus, it is to be understood that,within the scope of the appended claims, the invention may be practicedotherwise than as described hereinabove.

1. A method of impairment mitigation in a communications systemcomprising: generating at least one error estimate of a signal;determining a channel fidelity metric using the at least one errorestimate; and decoding the signal using the channel fidelity metric. 2.The method of claim 1 wherein the signal comprises one of at least onedigital sample or at least one symbol.
 3. The method of claim 2 furthercomprising storing the channel fidelity metric.
 4. The method of claim 3further comprising determining a transmit waveform using the storedfidelity metric.
 5. The method of claim 3 further comprising selecting areceiver algorithm using the stored fidelity metric.
 6. The method ofclaim 1 wherein the signal comprises at least one symbol and whereingenerating at least one error estimate comprises: determining at leastone constellation point closest to the at least one symbol; determininga distance between the at least symbol and the at least oneconstellation point; and squaring the distance.
 7. The method of claim 1wherein the signal comprises at least one symbol, and whereindetermining a channel fidelity metric comprises: comparing the at leastone error estimate to at least one predetermined threshold.
 8. Themethod of claim 7 further comprising generating a first indication ifthe at least one error estimate is above the at least one predeterminedthreshold and a second indication if the at least one error estimate isnot above the at least one predetermined threshold.
 9. The method ofclaim 7 comprising keeping a select symbol if the at least one errorestimate is below the at least one predetermined threshold, and erasingthe select symbol if the at least one error estimate is above the atleast one threshold.
 10. The method of claim 1 wherein the signalcomprises a sequence of symbols, and wherein a first portion of thesequence comprises at least one select symbol, and comprising:determining a first error estimate from the sequence of symbols;determining a second error estimate from a second portion of thesequence of symbols including the at least one select symbol;determining a third error estimate from a third portion of the sequenceof symbols including the at least one select symbol; comparing the firsterror estimate to a first predetermined threshold, and the second andthird error estimates to a second predetermined threshold; and erasingat least a portion of the at least one select symbol if the first errorestimate is above the first predetermined threshold and if the secondand third error estimates are above the second predetermined threshold.11. A method of impairment mitigation in a communications systemcomprising: generating at least one error estimate of a signal receivedfrom a channel; determining if the channel is degraded based on the atleast one error estimate; erasing a select symbol of the signal if thechannel is degraded; and decoding the signal.
 12. The method of claim 11further comprising keeping the select symbol if the channel is notdegraded.
 13. The method of claim 11 further comprising decoding thesignal and encoding the signal before generating the at least one errorestimate.
 14. The method of claim 11 wherein the signal comprises atleast one symbol and wherein generating at least one error estimatecomprises: determining at least one constellation point closest to theat least one symbol; determining a distance between the at least symboland the at least one constellation point; and squaring the distance. 15.The method of claim 11 wherein the signal comprises at least one symbol,and wherein determining whether the channel is degraded comprises:comparing the at least one error estimate to at least one predeterminedthreshold.
 16. The method of claim 15 wherein the channel is degraded ifthe at least one error estimate is above the at least one predeterminedthreshold.
 17. The method of claim 11 wherein the signal comprises asequence of symbols, and wherein a first portion of the sequencecomprises at least one select symbol, and comprising: determining afirst error estimate from the sequence of symbols; determining a seconderror estimate from a second portion of the sequence of symbolsincluding the at least one select symbol; determining a third errorestimate from a third portion of the sequence of symbols including theat least one select symbol; comparing the first error estimate to afirst predetermined threshold, and the second and third error estimatesto a second predetermined threshold; and generating an indication thatthe channel is degraded if the first error estimate is above the firstpredetermined threshold and if the second and third error estimates areabove the second predetermined threshold.
 18. A method of impairmentmitigation in a communications system comprising: generating at leastone error estimate of a signal; determining a channel fidelity metricusing the at least one error estimate; generating a branch metric for adecoder; modifying the branch metric based on the channel fidelitymetric; and decoding the signal using the modified branch metric. 19.The method of claim 18 wherein the decoder is a Viterbi decoder.
 20. Themethod of claim 18 wherein modifying the branch metric comprises settingthe branch metric to a low probability if the fidelity metric indicatesa degraded channel.